Compression of Functional Gradients During Early Non-REM Sleep: Python code for functional connectivity density and gradient mapping in CIFTI space
Year: 2026
Type: software
Abstract: Python code for functional connectivity density and functional gradient mapping in CIFTI spaceThis Figshare item contains Python code used in the manuscript submitted to PNAS Nexus examining sleep-related changes in macroscale functional brain organization. The code implements analyses in CIFTI space to compute functional connectivity density maps and functional gradient maps derived from resting-state functional MRI data.The functional connectivity density code computes voxelwise or grayordinate-level connectivity density measures from functional connectivity matrices or time series data, providing summary maps of local and/or global functional connectivity organization. The functional gradient mapping code estimates low-dimensional axes of macroscale cortical functional organization using diffusion embedding, enabling characterization of the brain’s principal functional gradients and sleep-related changes in cortical hierarchy. The code was used to generate derived measures reported in the associated manuscript, including functional connectivity density maps, principal gradient maps, gradient range or compression/expansion metrics, parcelwise or network-level gradient summaries, and statistical outputs used in the manuscript figures and tables. The scripts are provided to support transparency, reproducibility, and reuse of the reported analyses. They are intended for researchers interested in functional connectivity density mapping, CIFTI-based neuroimaging workflows, diffusion embedding, cortical gradients, and brain-state-dependent changes in large-scale functional organization.No raw neuroimaging data are included in this Figshare item. Users should review the script headers and comments for input requirements, software dependencies, file naming conventions, and analysis parameters.Please cite the associated PNAS Nexus manuscript when using or adapting this code. Users should also cite relevant methodological work on functional connectivity density mapping and functional gradient mapping, including:Tomasi D, Volkow ND. Functional connectivity density mapping. Proc Natl Acad Sci U S A. 2010;107(21):9885–9890. doi: 10.1073/pnas.1001414107Margulies DS et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A. 2016;113(44):12574–12579. doi: 10.1073/pnas.1608282113
Source: Figshare
Author: Tomasi, Dardo
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7164845289